Chapter 8 HMSC analysis

8.1 Load data

load("data/data.Rdata")
load("hmsc/fit_model1_250_10.Rdata")

8.2 Variance partitioning

# Compute variance partitioning
varpart=computeVariancePartitioning(m)

varpart$vals %>%
   as.data.frame() %>%
   rownames_to_column(var="variable") %>%
   pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
   mutate(variable=factor(variable, levels=rev(c("origin","sex","logseqdepth","Random: location")))) %>%
   group_by(variable) %>%
   summarise(mean=mean(value)*100,sd=sd(value)*100) %>%
   tt()

variable	mean	sd
Random: location	37.900015	25.317903
logseqdepth	56.110626	25.796874
sex	4.937460	5.612719
origin	1.051899	1.282563

# Basal tree
varpart_tree <- genome_tree

#Varpart table
varpart_table <- varpart$vals %>%
   as.data.frame() %>%
   rownames_to_column(var="variable") %>%
   pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
   mutate(genome=factor(genome, levels=rev(varpart_tree$tip.label))) %>%
   mutate(variable=factor(variable, levels=rev(c("origin","sex","logseqdepth","Random: location"))))

#Phylums
phylum_colors <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
    mutate(phylum=str_remove_all(phylum, "p__"))%>%
  right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
    filter(genome %in% varpart_tree$tip.label) %>%
    arrange(match(genome, varpart_tree$tip.label)) %>%
    mutate(phylum = factor(phylum, levels = unique(phylum))) %>%
    column_to_rownames(var = "genome") %>%
    dplyr::select(phylum)

colors_alphabetic <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
    mutate(phylum=str_remove_all(phylum, "p__"))%>%
  right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
    filter(genome %in% varpart_tree$tip.label) %>%
    arrange(match(genome, varpart_tree$tip.label)) %>%
     dplyr::select(phylum, colors) %>%
    unique() %>%
    arrange(phylum) %>%
    dplyr::select(colors) %>%
    pull()

# Basal ggtree
varpart_tree <- varpart_tree %>%
        force.ultrametric(.,method="extend") %>%
        ggtree(., size = 0.3)

***************************************************************
*                          Note:                              *
*    force.ultrametric does not include a formal method to    *
*    ultrametricize a tree & should only be used to coerce    *
*   a phylogeny that fails is.ultrametric due to rounding --  *
*    not as a substitute for formal rate-smoothing methods.   *
***************************************************************

# Add phylum colors next to the tree tips
varpart_tree <- gheatmap(varpart_tree, phylum_colors, offset=-0.2, width=0.1, colnames=FALSE) +
   scale_fill_manual(values=colors_alphabetic)+
      labs(fill="Phylum")

#Reset fill scale to use a different colour profile in the heatmap
varpart_tree <- varpart_tree + new_scale_fill()

# Add variance stacked barplot
vertical_tree <-  varpart_tree +
       scale_fill_manual(values=c("#506a96","#cccccc","#be3e2b","#f6de6c"))+
        geom_fruit(
             data=varpart_table,
             geom=geom_bar,
             mapping = aes(x=value, y=genome, fill=variable, group=variable),
             pwidth = 2,
             offset = 0.05,
             width= 1,
             orientation="y",
             stat="identity")+
      labs(fill="Variable")

vertical_tree

8.3 Posterior estimates

# Select desired support threshold
support=0.9
negsupport=1-support

# Basal tree
postestimates_tree <- genome_tree

# Posterior estimate table
post_beta <- getPostEstimate(hM=m, parName="Beta")$support %>%
    as.data.frame() %>%
    mutate(variable=m$covNames) %>%
    pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
    mutate(genome=factor(genome, levels=rev(postestimates_tree$tip.label))) %>%
    mutate(value = case_when(
          value >= support ~ "Positive",
          value <= negsupport ~ "Negative",
          TRUE ~ "Neutral")) %>%
    mutate(value=factor(value, levels=c("Positive","Neutral","Negative"))) %>%
    pivot_wider(names_from = variable, values_from = value) %>%
    #select(genome,sp_vulgaris,area_semi,area_urban,sp_vulgarisxarea_semi,sp_vulgarisxarea_urban,season_spring,season_winter,sp_vulgarisxseason_spring,sp_vulgarisxseason_winter) %>%
    column_to_rownames(var="genome")

#Phylums
phylum_colors <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
    mutate(phylum=str_remove_all(phylum, "p__")) %>%
    right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
    filter(genome %in% postestimates_tree$tip.label) %>%
    arrange(match(genome, postestimates_tree$tip.label)) %>%
    mutate(phylum = factor(phylum, levels = unique(phylum))) %>%
    column_to_rownames(var = "genome") %>%
    dplyr::select(phylum)


colors_alphabetic <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
    mutate(phylum=str_remove_all(phylum, "p__")) %>%
    right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
    filter(genome %in% postestimates_tree$tip.label) %>%
    arrange(match(genome, postestimates_tree$tip.label)) %>%
     dplyr::select(phylum, colors) %>%
    unique() %>%
    arrange(phylum) %>%
    dplyr::select(colors) %>%
    pull()

# Basal ggtree
postestimates_tree <- postestimates_tree %>%
        force.ultrametric(.,method="extend") %>%
        ggtree(., size = 0.3)

***************************************************************
*                          Note:                              *
*    force.ultrametric does not include a formal method to    *
*    ultrametricize a tree & should only be used to coerce    *
*   a phylogeny that fails is.ultrametric due to rounding --  *
*    not as a substitute for formal rate-smoothing methods.   *
***************************************************************

#Add phylum colors next to the tree tips
postestimates_tree <- gheatmap(postestimates_tree, phylum_colors, offset=-0.2, width=0.1, colnames=FALSE) +
      scale_fill_manual(values=colors_alphabetic)+
      labs(fill="Phylum")

#Reset fill scale to use a different colour profile in the heatmap
postestimates_tree <- postestimates_tree + new_scale_fill()

# Add posterior significant heatmap

postestimates_tree <- gheatmap(postestimates_tree, post_beta, offset=0, width=0.5, colnames=TRUE, colnames_position="top",colnames_angle=90, colnames_offset_y=1, hjust=0) +
        scale_fill_manual(values=c("#be3e2b","#f4f4f4","#b2b530"))+
        labs(fill="Trend")

postestimates_tree +
        vexpand(.25, 1) # expand top 

8.3.1 Positively associated genomes

getPostEstimate(hM=m, parName="Beta")$support %>%
    as.data.frame() %>%
    mutate(variable=m$covNames) %>%
    pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
    mutate(trend = case_when(
          value >= support ~ "Positive",
          value <= negsupport ~ "Negative",
          TRUE ~ "Neutral")) %>%
    filter(variable=="originTame", trend=="Positive") %>%
    arrange(-value) %>%
    left_join(genome_metadata,by=join_by(genome==genome)) %>%
    dplyr::select(genome,phylum,class,order,family,species,value) %>%
    arrange(phylum, class, family)%>%
    tt()

genome	phylum	class	order	family	species	value
bin_CaboVerde.50	Actinomycetota	Actinomycetia	Actinomycetales	Actinomycetaceae	NA	0.953
bin_Aruba.25	Actinomycetota	Actinomycetia	Actinomycetales	Bifidobacteriaceae	Bifidobacterium pseudocatenulatum	0.995
bin_Aruba.2	Actinomycetota	Actinomycetia	Actinomycetales	Bifidobacteriaceae	Bifidobacterium adolescentis	0.994
bin_Aruba.6	Actinomycetota	Actinomycetia	Actinomycetales	Bifidobacteriaceae	Bifidobacterium longum	0.985
bin_Denmark.14	Actinomycetota	Actinomycetia	Actinomycetales	Bifidobacteriaceae	Bifidobacterium gallinarum	0.950
bin_Aruba.47	Actinomycetota	Actinomycetia	Mycobacteriales	Mycobacteriaceae	NA	0.964
bin_Aruba.57	Actinomycetota	Actinomycetia	Mycobacteriales	Mycobacteriaceae	Corynebacterium pyruviciproducens	0.931
bin_Aruba.51	Actinomycetota	Coriobacteriia	Coriobacteriales	Atopobiaceae	Parolsenella sp900544655	0.969
bin_Denmark.44	Actinomycetota	Coriobacteriia	Coriobacteriales	Atopobiaceae	UBA7748 sp900314535	0.951
bin_Aruba.14	Actinomycetota	Coriobacteriia	Coriobacteriales	Coriobacteriaceae	Collinsella stercoris	1.000
bin_Denmark.4	Actinomycetota	Coriobacteriia	Coriobacteriales	Coriobacteriaceae	Collinsella stercoris	1.000
bin_Aruba.21	Actinomycetota	Coriobacteriia	Coriobacteriales	Coriobacteriaceae	NA	0.999
bin_Brazil.61	Actinomycetota	Coriobacteriia	Coriobacteriales	Coriobacteriaceae	Collinsella tanakaei	0.998
bin_Aruba.39	Actinomycetota	Coriobacteriia	Coriobacteriales	Coriobacteriaceae	Collinsella sp902362275	0.992
bin_Spain.84	Actinomycetota	Coriobacteriia	Coriobacteriales	Coriobacteriaceae	Collinsella sp900555555	0.992
bin_Brazil.151	Actinomycetota	Coriobacteriia	Coriobacteriales	Coriobacteriaceae	Collinsella intestinalis	0.991
bin_Denmark.127	Actinomycetota	Coriobacteriia	Coriobacteriales	Coriobacteriaceae	Collinsella sp900548365	0.991
bin_Denmark.17	Actinomycetota	Coriobacteriia	Coriobacteriales	Coriobacteriaceae	Collinsella bouchesdurhonensis	0.983
bin_Brazil.81	Bacillota	Bacilli	RFN20	CAG-826	NA	0.907
bin_Brazil.109	Bacillota_A	Clostridia	Oscillospirales	Butyricicoccaceae	AM07-15 sp003477405	0.936
bin_Aruba.28	Bacillota_A	Clostridia	Oscillospirales	Oscillospiraceae	NA	0.995
bin_Malaysia.9	Bacillota_A	Clostridia	Oscillospirales	Oscillospiraceae	Dysosmobacter welbionis	0.986
bin_Malaysia.116	Bacillota_A	Clostridia	Oscillospirales	Oscillospiraceae	Flavonifractor plautii	0.981
bin_Malaysia.34	Bacillota_A	Clostridia	Oscillospirales	Oscillospiraceae	Lawsonibacter sp000177015	0.954
bin_Aruba.54	Bacillota_A	Clostridia	Tissierellales	Peptoniphilaceae	NA	0.959
bin_CaboVerde.35	Bacillota_A	Clostridia	Tissierellales	Peptoniphilaceae	NA	0.930
bin_Aruba.36	Bacillota_A	Clostridia	Tissierellales	Peptoniphilaceae	Peptoniphilus_C sp902363535	0.913
bin_Aruba.52	Bacillota_A	Clostridia	Tissierellales	Peptoniphilaceae	NA	0.906
bin_Malaysia.26	Bacillota_A	Clostridia	Oscillospirales	Ruminococcaceae	NA	0.976
bin_Aruba.29	Bacillota_A	Clostridia	Oscillospirales	Ruminococcaceae	Gemmiger variabilis_C	0.941
bin_Malaysia.103	Bacillota_C	Negativicutes	Acidaminococcales	Acidaminococcaceae	Acidaminococcus intestini	0.942
bin_Malaysia.8	Bacillota_C	Negativicutes	Veillonellales	Dialisteraceae	Dialister sp900543165	0.981
bin_Denmark.60	Bacillota_C	Negativicutes	Veillonellales	Dialisteraceae	Allisonella histaminiformans	0.938
bin_Aruba.64	Bacillota_C	Negativicutes	Veillonellales	Megasphaeraceae	Megasphaera sp000417505	0.970
bin_Brazil.58	Bacillota_C	Negativicutes	Veillonellales	Megasphaeraceae	Megasphaera elsdenii	0.969
bin_CaboVerde.38	Bacillota_C	Negativicutes	Veillonellales	Megasphaeraceae	Megasphaera sp000417505	0.961
bin_Malaysia.81	Bacillota_C	Negativicutes	Veillonellales	Megasphaeraceae	Megasphaera stantonii	0.943
bin_Malaysia.96	Bacillota_C	Negativicutes	Veillonellales	Megasphaeraceae	Caecibacter sp003467125	0.919
bin_Brazil.163	Bacteroidota	Bacteroidia	Bacteroidales	Bacteroidaceae	Prevotella lascolaii	0.992
bin_CaboVerde.18	Bacteroidota	Bacteroidia	Bacteroidales	Bacteroidaceae	Prevotella copri	0.981
bin_Aruba.10	Bacteroidota	Bacteroidia	Bacteroidales	Bacteroidaceae	Prevotella sp900544825	0.978
bin_Brazil.5	Bacteroidota	Bacteroidia	Bacteroidales	Bacteroidaceae	Prevotella sp900540415	0.929
bin_Spain.21	Bacteroidota	Bacteroidia	Bacteroidales	Bacteroidaceae	Phocaeicola coprophilus	0.929
bin_Malaysia.18	Bacteroidota	Bacteroidia	Bacteroidales	Bacteroidaceae	Prevotellamassilia sp000437675	0.924
bin_Malaysia.117	Bacteroidota	Bacteroidia	Bacteroidales	Bacteroidaceae	Prevotellamassilia sp900541335	0.919
bin_Malaysia.77	Bacteroidota	Bacteroidia	Bacteroidales	Bacteroidaceae	Phocaeicola sp900542985	0.913
bin_Brazil.75	Campylobacterota	Campylobacteria	Campylobacterales	Helicobacteraceae	NA	0.964
bin_Malaysia.61	Campylobacterota	Campylobacteria	Campylobacterales	Helicobacteraceae	Helicobacter_C labetoulli	0.964
bin_Brazil.128	Campylobacterota	Campylobacteria	Campylobacterales	Helicobacteraceae	NA	0.963
bin_Brazil.70	Desulfobacterota	Desulfovibrionia	Desulfovibrionales	Desulfovibrionaceae	Mailhella sp003150275	0.944
bin_Brazil.146	Pseudomonadota	Alphaproteobacteria	RF32	CAG-239	CAG-495 sp001917125	0.969
bin_Brazil.9	Pseudomonadota	Alphaproteobacteria	RF32	CAG-239	CAG-495 sp000436375	0.919
bin_Aruba.15	Pseudomonadota	Gammaproteobacteria	Burkholderiales	Burkholderiaceae	NA	0.963
bin_Brazil.51	Pseudomonadota	Gammaproteobacteria	Burkholderiales	Burkholderiaceae	Sutterella wadsworthensis_A	0.963
bin_Spain.43	Pseudomonadota	Gammaproteobacteria	Burkholderiales	Burkholderiaceae	CAG-521 sp000437635	0.959
bin_Brazil.64	Pseudomonadota	Gammaproteobacteria	Burkholderiales	Burkholderiaceae	NA	0.948
bin_Brazil.92	Pseudomonadota	Gammaproteobacteria	Burkholderiales	Burkholderiaceae	CAG-521 sp000437635	0.948
bin_Brazil.15	Pseudomonadota	Gammaproteobacteria	Burkholderiales	Burkholderiaceae	NA	0.930
bin_CaboVerde.33	Pseudomonadota	Gammaproteobacteria	Enterobacterales	Succinivibrionaceae	Anaerobiospirillum sp900543125	0.995
bin_Brazil.82	Pseudomonadota	Gammaproteobacteria	Enterobacterales	Succinivibrionaceae	Anaerobiospirillum succiniciproducens	0.990
bin_CaboVerde.55	Pseudomonadota	Gammaproteobacteria	Enterobacterales	Succinivibrionaceae	Anaerobiospirillum_A thomasii	0.939

8.3.2 Negatively associated genomes

getPostEstimate(hM=m, parName="Beta")$support %>%
    as.data.frame() %>%
    mutate(variable=m$covNames) %>%
    pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
    mutate(trend = case_when(
          value >= support ~ "Positive",
          value <= negsupport ~ "Negative",
          TRUE ~ "Neutral")) %>%
    filter(variable=="originTame", trend=="Negative") %>%
    arrange(value) %>%
    left_join(genome_metadata,by=join_by(genome==genome)) %>%
    dplyr::select(genome,phylum,class,order,family,species,value) %>%
    arrange(phylum,class,family)%>%
    tt()

genome	phylum	class	order	family	species	value
bin_Denmark.27	Bacillota	Bacilli	Lactobacillales	Enterococcaceae	Enterococcus_B hirae	0.000
bin_Brazil.12	Bacillota	Bacilli	Lactobacillales	Enterococcaceae	Enterococcus faecalis	0.001
bin_Brazil.170	Bacillota	Bacilli	Lactobacillales	Enterococcaceae	NA	0.014
bin_CaboVerde.24	Bacillota	Bacilli	Lactobacillales	Enterococcaceae	Enterococcus_E cecorum	0.020
bin_CaboVerde.16	Bacillota	Bacilli	Lactobacillales	Lactobacillaceae	Limosilactobacillus reuteri	0.000
bin_Denmark.56	Bacillota	Bacilli	Lactobacillales	Lactobacillaceae	Levilactobacillus brevis	0.000
bin_Malaysia.72	Bacillota	Bacilli	Lactobacillales	Lactobacillaceae	Limosilactobacillus reuteri	0.000
bin_CaboVerde.60	Bacillota	Bacilli	Lactobacillales	Lactobacillaceae	Ligilactobacillus agilis	0.001
bin_Malaysia.127	Bacillota	Bacilli	Lactobacillales	Lactobacillaceae	Ligilactobacillus agilis	0.001
bin_Denmark.61	Bacillota	Bacilli	Lactobacillales	Lactobacillaceae	Latilactobacillus sakei	0.003
bin_Malaysia.35	Bacillota	Bacilli	Lactobacillales	Lactobacillaceae	Ligilactobacillus animalis	0.003
bin_Malaysia.4	Bacillota	Bacilli	Lactobacillales	Lactobacillaceae	Ligilactobacillus ruminis	0.035
bin_CaboVerde.14	Bacillota	Bacilli	Lactobacillales	Lactobacillaceae	Lactobacillus acidophilus	0.067
bin_Aruba.18	Bacillota	Bacilli	Lactobacillales	Streptococcaceae	Streptococcus lutetiensis	0.001
bin_Brazil.22	Bacillota	Bacilli	Lactobacillales	Streptococcaceae	Streptococcus pasteurianus	0.001
bin_Denmark.117	Bacillota	Bacilli	Lactobacillales	Streptococcaceae	Lactococcus garvieae	0.001
bin_Denmark.113	Bacillota	Bacilli	Lactobacillales	Streptococcaceae	Streptococcus alactolyticus	0.002
bin_Brazil.69	Bacillota_A	Clostridia	Oscillospirales	Acutalibacteraceae	Clostridium_A leptum	0.097
bin_Denmark.93	Bacillota_A	Clostridia	Lachnospirales	Anaerotignaceae	Anaerotignum sp001304995	0.074
bin_CaboVerde.3	Bacillota_A	Clostridia	Clostridiales	Clostridiaceae	Clostridium_P perfringens	0.000
bin_Denmark.42	Bacillota_A	Clostridia	Clostridiales	Clostridiaceae	Clostridium_P perfringens	0.000
bin_Brazil.136	Bacillota_A	Clostridia	Clostridiales	Clostridiaceae	Clostridium thermobutyricum	0.041
bin_Brazil.3	Bacillota_A	Clostridia	Lachnospirales	Lachnospiraceae	Faecalimonas sp900555395	0.026
bin_Brazil.89	Bacillota_A	Clostridia	Lachnospirales	Lachnospiraceae	Faecalimonas sp900550235	0.034
bin_Denmark.63	Bacillota_A	Clostridia	Lachnospirales	Lachnospiraceae	Anaerostipes hadrus	0.040
bin_Denmark.19	Bacillota_A	Clostridia	Lachnospirales	Lachnospiraceae	Faecalimonas sp900556835	0.052
bin_Denmark.118	Bacillota_A	Clostridia	Lachnospirales	Lachnospiraceae	Dorea_A longicatena	0.067
bin_Malaysia.108	Bacillota_A	Clostridia	Lachnospirales	Lachnospiraceae	NA	0.081
bin_Brazil.113	Bacillota_A	Clostridia	Lachnospirales	Lachnospiraceae	Anaerobutyricum sp900754855	0.095
bin_Denmark.50	Bacillota_A	Clostridia	Peptostreptococcales	Peptostreptococcaceae	Peptacetobacter sp900539645	0.053
bin_Brazil.107	Bacillota_A	Clostridia	Monoglobales_A	UBA1381	CAG-41 sp900066215	0.016
bin_Brazil.159	Fusobacteriota	Fusobacteriia	Fusobacteriales	Fusobacteriaceae	Fusobacterium_B sp900541465	0.040
bin_Brazil.140	Fusobacteriota	Fusobacteriia	Fusobacteriales	Fusobacteriaceae	Fusobacterium_A sp900543175	0.046
bin_Malaysia.6	Fusobacteriota	Fusobacteriia	Fusobacteriales	Fusobacteriaceae	Fusobacterium_B sp900545035	0.088

8.3.3 Estimate - support plot

estimate <- getPostEstimate(hM=m, parName="Beta")$mean %>%
    as.data.frame() %>%
    mutate(variable=m$covNames) %>%
    filter(variable=="originTame") %>%
    pivot_longer(!variable, names_to = "genome", values_to = "mean") %>%
    dplyr::select(genome,mean)

support <- getPostEstimate(hM=m, parName="Beta")$support %>%
    as.data.frame() %>%
    mutate(variable=m$covNames) %>%
    filter(variable=="originTame") %>%
    pivot_longer(!variable, names_to = "genome", values_to = "support") %>%
    dplyr::select(genome,support)

phylum_colors <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
  right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
  filter(genome %in% estimate$genome) %>%
  arrange(match(genome, estimate$genome)) %>%
  dplyr::select(phylum, colors) %>%
  unique() %>%
  arrange(phylum) %>%
  dplyr::select(colors) %>%
  pull()

Rows: 202 Columns: 2
── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr (2): phylum, colors

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

inner_join(estimate,support,by=join_by(genome==genome)) %>%
    mutate(significance=ifelse(support >= 0.9 | support <= 0.1,1,0)) %>%
    mutate(support=ifelse(mean<0,1-support,support)) %>%
    left_join(genome_metadata, by = join_by(genome == genome)) %>%
    mutate(phylum = ifelse(support > 0.9, phylum, NA)) %>%
    ggplot(aes(x=mean,y=support,color=phylum))+
      geom_point(alpha=0.7, shape=16, size=3)+
      scale_color_manual(values = phylum_colors) +
      geom_vline(xintercept = 0) +
      xlim(c(-0.4,0.4)) +
      labs(x = "Beta regression coefficient", y = "Posterior probability") +
      theme_minimal()+
       theme(legend.position = "none")

8.4 Correlations

#Compute the residual correlation matrix
OmegaCor = computeAssociations(m)

# Refernece tree (for sorting genomes)
genome_tree_subset <- genome_tree %>%
        keep.tip(., tip=m$spNames) 


#Co-occurrence matrix at the animal level
supportLevel = 0.95
toPlot = ((OmegaCor[[1]]$support>supportLevel)
          + (OmegaCor[[1]]$support<(1-supportLevel))>0)*OmegaCor[[1]]$mean

matrix <- toPlot %>% 
      as.data.frame() %>%
      rownames_to_column(var="genome1") %>%
      pivot_longer(!genome1, names_to = "genome2", values_to = "cor") %>%
      mutate(genome1= factor(genome1, levels=genome_tree_subset$tip.label)) %>%
      mutate(genome2= factor(genome2, levels=genome_tree_subset$tip.label)) %>%
      ggplot(aes(x = genome1, y = genome2, fill = cor)) +
            geom_tile() + 
            scale_fill_gradient2(low = "#be3e2b",
                       mid = "#f4f4f4",
                       high = "#b2b530")+
            theme_void()

htree <- genome_tree_subset %>%
  force.ultrametric(.,method="extend") %>%
  ggtree(.)

***************************************************************
*                          Note:                              *
*    force.ultrametric does not include a formal method to    *
*    ultrametricize a tree & should only be used to coerce    *
*   a phylogeny that fails is.ultrametric due to rounding --  *
*    not as a substitute for formal rate-smoothing methods.   *
***************************************************************

vtree <- genome_tree_subset %>%
  force.ultrametric(.,method="extend") %>%
  ggtree(.)

***************************************************************
*                          Note:                              *
*    force.ultrametric does not include a formal method to    *
*    ultrametricize a tree & should only be used to coerce    *
*   a phylogeny that fails is.ultrametric due to rounding --  *
*    not as a substitute for formal rate-smoothing methods.   *
***************************************************************

#create composite figure
grid.arrange(grobs = list(matrix,vtree),
             layout_matrix = rbind(c(2,1,1,1,1,1,1,1,1,1,1,1),
                                   c(2,1,1,1,1,1,1,1,1,1,1,1),
                                   c(2,1,1,1,1,1,1,1,1,1,1,1),
                                   c(2,1,1,1,1,1,1,1,1,1,1,1),
                                   c(2,1,1,1,1,1,1,1,1,1,1,1),
                                   c(2,1,1,1,1,1,1,1,1,1,1,1),
                                   c(2,1,1,1,1,1,1,1,1,1,1,1),
                                   c(2,1,1,1,1,1,1,1,1,1,1,1),
                                   c(2,1,1,1,1,1,1,1,1,1,1,1),
                                   c(2,1,1,1,1,1,1,1,1,1,1,1),
                                   c(2,1,1,1,1,1,1,1,1,1,1,1)))

8.5 Predict responses

# Select modelchain of interest
load("hmsc/fit_model1_250_10.Rdata")

gradient = c("domestic","feral")
gradientlength = length(gradient)

#Treatment-specific gradient predictions
pred <- constructGradient(m, 
                      focalVariable = "origin", 
                      non.focalVariables = list(logseqdepth=list(1),location=list(1))) %>%
            predict(m, Gradient = ., expected = TRUE) %>%
            do.call(rbind,.) %>%
            as.data.frame() %>%
            mutate(origin=rep(gradient,1000)) %>%
            pivot_longer(!origin,names_to = "genome", values_to = "value")

# weights:  9 (4 variable)
initial  value 101.072331 
final  value 91.392443 
converged

8.5.0.1 Element level

elements_table <- genome_gifts %>%
    to.elements(., GIFT_db) %>%
    as.data.frame()

community_elements <- pred %>%
  group_by(origin, genome) %>%
  mutate(row_id = row_number()) %>%
  pivot_wider(names_from = genome, values_from = value) %>%
  ungroup() %>%
  group_split(row_id) %>%
  as.list() %>%
  lapply(., FUN = function(x){x %>%
    dplyr::select(-row_id) %>%
    column_to_rownames(var = "origin") %>%
    as.data.frame() %>%
    exp() %>%
    t() %>%
    tss() %>%
    to.community(elements_table,.,GIFT_db) %>% 
    as.data.frame() %>%
    rownames_to_column(var="origin")
   })

calculate_slope <- function(x) {
  lm_fit <- lm(unlist(x) ~ seq_along(unlist(x)))
  coef(lm_fit)[2]
}

element_predictions <- map_dfc(community_elements, function(mat) {
      mat %>%
        column_to_rownames(var = "origin") %>%
        t() %>%
        as.data.frame() %>%
        rowwise() %>%
        mutate(slope = calculate_slope(c_across(everything()))) %>%
        dplyr::select(slope) }) %>%
      t() %>%
      as.data.frame() %>%
      set_names(colnames(community_elements[[1]])[-1]) %>%
      rownames_to_column(var="iteration") %>%
      pivot_longer(!iteration, names_to="trait",values_to="value") %>%
      group_by(trait) %>%
      summarise(mean=mean(value),
        p1 = quantile(value, probs = 0.1),
        p9 = quantile(value, probs = 0.9),
        positive_support = sum(value > 0)/1000,
        negative_support = sum(value < 0)/1000) %>%
      arrange(-positive_support)

8.5.1 Positive associated functions at element level

# Positively associated

unique_funct_db<- GIFT_db[c(2,4,5,6)] %>% 
  distinct(Code_element, .keep_all = TRUE)


element_predictions %>%
  filter(mean >0) %>%
  arrange(-positive_support) %>%
  filter(positive_support>=0.9) %>%
  left_join(unique_funct_db, by = join_by(trait == Code_element))%>%
  arrange(Domain,Function)%>%
  tt()

trait	mean	p1	p9	positive_support	negative_support	Domain	Function	Element
B0103	0.008721055	5.858948e-05	0.016907646	0.902	0.098	Biosynthesis	Nucleic acid biosynthesis	UDP/UTP
D0504	0.004386196	1.313410e-04	0.009500262	0.911	0.089	Degradation	Amino acid degradation	Methionine
D0906	0.003670175	1.379577e-04	0.008121394	0.926	0.074	Degradation	Antibiotic degradation	Fosfomycin
D0205	0.012779324	2.341355e-03	0.023302092	0.954	0.046	Degradation	Polysaccharide degradation	Pectin
D0208	0.010331977	1.709235e-03	0.018996311	0.935	0.065	Degradation	Polysaccharide degradation	Mixed-Linkage glucans

element_predictions %>%
  filter(mean <0) %>%
  arrange(-negative_support) %>%
  filter(negative_support>=0.9) %>%
  left_join(unique_funct_db, by = join_by(trait == Code_element))%>%
  arrange(Domain,Function)%>%
  tt()

trait	mean	p1	p9	positive_support	negative_support	Domain	Function	Element
B0219	-0.004243274	-0.009449620	-2.360171e-04	0.041	0.959	Biosynthesis	Amino acid biosynthesis	GABA
B0214	-0.020976378	-0.036675942	-4.271212e-03	0.069	0.931	Biosynthesis	Amino acid biosynthesis	Glutamate
B0302	-0.005039082	-0.010242469	-5.152291e-04	0.032	0.968	Biosynthesis	Amino acid derivative biosynthesis	Betaine
B0310	-0.012921300	-0.022832185	-3.166348e-03	0.037	0.963	Biosynthesis	Amino acid derivative biosynthesis	Tryptamine
B0303	-0.011717187	-0.021302206	-2.302478e-03	0.059	0.941	Biosynthesis	Amino acid derivative biosynthesis	Ectoine
B0309	-0.007622324	-0.014859566	-3.193213e-04	0.092	0.908	Biosynthesis	Amino acid derivative biosynthesis	Putrescine
B0804	-0.016338997	-0.029159443	-2.639667e-03	0.057	0.943	Biosynthesis	Aromatic compound biosynthesis	Dipicolinate
B0603	-0.016187723	-0.031768524	-2.015090e-03	0.076	0.924	Biosynthesis	Organic anion biosynthesis	Citrate
B0601	-0.009214752	-0.018849619	-8.066553e-04	0.080	0.920	Biosynthesis	Organic anion biosynthesis	Succinate
B0401	-0.011952355	-0.022575997	-1.074075e-03	0.071	0.929	Biosynthesis	SCFA biosynthesis	Acetate
B0709	-0.002065553	-0.003638992	-5.805158e-04	0.032	0.968	Biosynthesis	Vitamin biosynthesis	Tocopherol/tocotorienol (E)
B0708	-0.024292457	-0.048067418	-2.361969e-04	0.098	0.902	Biosynthesis	Vitamin biosynthesis	Cobalamin (B12)
D0517	-0.004760845	-0.008589668	-1.196185e-03	0.031	0.969	Degradation	Amino acid degradation	Ornithine
D0508	-0.003427595	-0.007405302	-2.064998e-04	0.074	0.926	Degradation	Amino acid degradation	Lysine
D0903	-0.004207352	-0.009395988	-2.301420e-04	0.041	0.959	Degradation	Antibiotic degradation	Cephalosporin
D0908	-0.015747946	-0.028054674	-3.756246e-03	0.068	0.932	Degradation	Antibiotic degradation	Macrolide
D0601	-0.009353606	-0.017995591	-2.103861e-03	0.034	0.966	Degradation	Nitrogen compound degradation	Nitrate
D0611	-0.004207352	-0.009395988	-2.301420e-04	0.041	0.959	Degradation	Nitrogen compound degradation	Phenylethylamine
D0603	-0.001979236	-0.003939691	-3.288703e-04	0.043	0.957	Degradation	Nitrogen compound degradation	Urate
D0610	-0.002989440	-0.004961091	-8.944614e-04	0.052	0.948	Degradation	Nitrogen compound degradation	Methylamine
D0606	-0.005920775	-0.011186099	-1.007489e-03	0.061	0.939	Degradation	Nitrogen compound degradation	Allantoin
D0612	-0.001692843	-0.003140507	-1.717185e-05	0.097	0.903	Degradation	Nitrogen compound degradation	Hypotaurine
D0801	-0.001729950	-0.002274583	-9.185503e-05	0.012	0.988	Degradation	Xenobiotic degradation	Toluene
D0802	-0.001729950	-0.002274583	-9.185503e-05	0.012	0.988	Degradation	Xenobiotic degradation	Xylene
D0807	-0.004248376	-0.008547901	-6.633122e-04	0.054	0.946	Degradation	Xenobiotic degradation	Catechol
D0817	-0.005084664	-0.010358431	-5.854535e-04	0.059	0.941	Degradation	Xenobiotic degradation	Trans-cinnamate
D0816	-0.005861200	-0.012171134	-8.084910e-05	0.093	0.907	Degradation	Xenobiotic degradation	Phenylacetate

8.5.2 Negatively associated functions at element level

element_predictions %>%
  filter(mean <0) %>%
  arrange(-negative_support) %>%
  filter(negative_support>=0.9) %>%
  left_join(unique_funct_db, by = join_by(trait == Code_element))%>%
  arrange(Domain,Function)%>%
  tt()

trait	mean	p1	p9	positive_support	negative_support	Domain	Function	Element
B0219	-0.004243274	-0.009449620	-2.360171e-04	0.041	0.959	Biosynthesis	Amino acid biosynthesis	GABA
B0214	-0.020976378	-0.036675942	-4.271212e-03	0.069	0.931	Biosynthesis	Amino acid biosynthesis	Glutamate
B0302	-0.005039082	-0.010242469	-5.152291e-04	0.032	0.968	Biosynthesis	Amino acid derivative biosynthesis	Betaine
B0310	-0.012921300	-0.022832185	-3.166348e-03	0.037	0.963	Biosynthesis	Amino acid derivative biosynthesis	Tryptamine
B0303	-0.011717187	-0.021302206	-2.302478e-03	0.059	0.941	Biosynthesis	Amino acid derivative biosynthesis	Ectoine
B0309	-0.007622324	-0.014859566	-3.193213e-04	0.092	0.908	Biosynthesis	Amino acid derivative biosynthesis	Putrescine
B0804	-0.016338997	-0.029159443	-2.639667e-03	0.057	0.943	Biosynthesis	Aromatic compound biosynthesis	Dipicolinate
B0603	-0.016187723	-0.031768524	-2.015090e-03	0.076	0.924	Biosynthesis	Organic anion biosynthesis	Citrate
B0601	-0.009214752	-0.018849619	-8.066553e-04	0.080	0.920	Biosynthesis	Organic anion biosynthesis	Succinate
B0401	-0.011952355	-0.022575997	-1.074075e-03	0.071	0.929	Biosynthesis	SCFA biosynthesis	Acetate
B0709	-0.002065553	-0.003638992	-5.805158e-04	0.032	0.968	Biosynthesis	Vitamin biosynthesis	Tocopherol/tocotorienol (E)
B0708	-0.024292457	-0.048067418	-2.361969e-04	0.098	0.902	Biosynthesis	Vitamin biosynthesis	Cobalamin (B12)
D0517	-0.004760845	-0.008589668	-1.196185e-03	0.031	0.969	Degradation	Amino acid degradation	Ornithine
D0508	-0.003427595	-0.007405302	-2.064998e-04	0.074	0.926	Degradation	Amino acid degradation	Lysine
D0903	-0.004207352	-0.009395988	-2.301420e-04	0.041	0.959	Degradation	Antibiotic degradation	Cephalosporin
D0908	-0.015747946	-0.028054674	-3.756246e-03	0.068	0.932	Degradation	Antibiotic degradation	Macrolide
D0601	-0.009353606	-0.017995591	-2.103861e-03	0.034	0.966	Degradation	Nitrogen compound degradation	Nitrate
D0611	-0.004207352	-0.009395988	-2.301420e-04	0.041	0.959	Degradation	Nitrogen compound degradation	Phenylethylamine
D0603	-0.001979236	-0.003939691	-3.288703e-04	0.043	0.957	Degradation	Nitrogen compound degradation	Urate
D0610	-0.002989440	-0.004961091	-8.944614e-04	0.052	0.948	Degradation	Nitrogen compound degradation	Methylamine
D0606	-0.005920775	-0.011186099	-1.007489e-03	0.061	0.939	Degradation	Nitrogen compound degradation	Allantoin
D0612	-0.001692843	-0.003140507	-1.717185e-05	0.097	0.903	Degradation	Nitrogen compound degradation	Hypotaurine
D0801	-0.001729950	-0.002274583	-9.185503e-05	0.012	0.988	Degradation	Xenobiotic degradation	Toluene
D0802	-0.001729950	-0.002274583	-9.185503e-05	0.012	0.988	Degradation	Xenobiotic degradation	Xylene
D0807	-0.004248376	-0.008547901	-6.633122e-04	0.054	0.946	Degradation	Xenobiotic degradation	Catechol
D0817	-0.005084664	-0.010358431	-5.854535e-04	0.059	0.941	Degradation	Xenobiotic degradation	Trans-cinnamate
D0816	-0.005861200	-0.012171134	-8.084910e-05	0.093	0.907	Degradation	Xenobiotic degradation	Phenylacetate

positive <- element_predictions %>%
  filter(mean >0) %>%
  arrange(mean) %>%
  filter(positive_support>=0.9) %>%
  dplyr::select(-negative_support) %>%
  rename(support=positive_support)

negative <- element_predictions %>%
  filter(mean <0) %>%
  arrange(mean) %>%
  filter(negative_support>=0.9) %>%
  dplyr::select(-positive_support) %>%
  rename(support=negative_support)

bind_rows(positive,negative) %>%
  left_join(GIFT_db,by=join_by(trait==Code_element)) %>%
  mutate(trait=factor(trait,levels=c(rev(positive$trait),rev(negative$trait)))) %>%
  ggplot(aes(x=mean, y=fct_rev(trait), xmin=p1, xmax=p9, color=Function)) +
      geom_point() +
      geom_errorbar() +
      xlim(c(-0.04,0.04)) +
      geom_vline(xintercept=0) +
      scale_color_manual(values = c("#debc14","#440526","#dc7c17","#172742","#debc14","#440526","#dc7c17","#172742","#357379","#6c7e2c","#d8dc69","#774d35","#db717d")) +
      theme_minimal() +
      labs(x="Regression coefficient",y="Functional trait")

table <- bind_rows(positive,negative) %>%
  left_join(unique_funct_db,by=join_by(trait==Code_element)) %>%
  mutate(trait=factor(trait,levels=c(rev(positive$trait),rev(negative$trait)))) 

table %>%
  mutate(Element=factor(Element,levels=c(table$Element))) %>%
  ggplot(aes(x=mean, y=fct_rev(Element), xmin=p1, xmax=p9, color=Function)) +
      geom_point() +
      geom_errorbar() +
      xlim(c(-0.04,0.04)) +
      geom_vline(xintercept=0) +
      scale_color_manual(values = c("#debc14","#440526","#dc7c17","#172742","#debc14","#440526","#dc7c17","#172742","#357379","#6c7e2c","#d8dc69","#774d35","#db717d")) +
      theme_minimal() +
      labs(x="Regression coefficient",y="Functional trait")

8.5.2.1 Function level

functions_table <- elements_table %>%
    to.functions(., GIFT_db) %>%
    as.data.frame()

community_functions <- pred %>%
  group_by(origin, genome) %>%
  mutate(row_id = row_number()) %>%
  pivot_wider(names_from = genome, values_from = value) %>%
  ungroup() %>%
  group_split(row_id) %>%
  as.list() %>%
  lapply(., FUN = function(x){x %>%
    dplyr::select(-row_id) %>%
    column_to_rownames(var = "origin") %>%
    as.data.frame() %>%
    exp() %>%
    t() %>%
    tss() %>%
    to.community(functions_table,.,GIFT_db) %>% 
    as.data.frame() %>%
    rownames_to_column(var="origin")
   })

#max-min option
calculate_slope <- function(x) {
  lm_fit <- lm(unlist(x) ~ seq_along(unlist(x)))
  coef(lm_fit)[2]
}

function_predictions <- map_dfc(community_functions, function(mat) {
      mat %>%
        column_to_rownames(var = "origin") %>%
        t() %>%
        as.data.frame() %>%
        rowwise() %>%
        mutate(slope = calculate_slope(c_across(everything()))) %>%
        dplyr::select(slope) }) %>%
      t() %>%
      as.data.frame() %>%
      set_names(colnames(community_functions[[1]])[-1]) %>%
      rownames_to_column(var="iteration") %>%
      pivot_longer(!iteration, names_to="trait",values_to="value") %>%
      group_by(trait) %>%
      summarise(mean=mean(value),
        p1 = quantile(value, probs = 0.1),
        p9 = quantile(value, probs = 0.9),
        positive_support = sum(value > 0)/1000,
        negative_support = sum(value < 0)/1000) %>%
      arrange(-positive_support)

# Positively associated
function_predictions %>%
  filter(mean >0) %>%
  arrange(-positive_support) %>%
  tt()

trait	mean	p1	p9	positive_support	negative_support
D02	8.580932e-03	-0.0029113778	0.0211120063	0.826	0.174
B08	7.733393e-03	-0.0033231015	0.0179292961	0.792	0.208
B01	1.376092e-03	-0.0058174809	0.0084040031	0.647	0.353
S01	8.140471e-04	-0.0125251212	0.0146119095	0.572	0.428
B09	5.381937e-05	-0.0005426244	0.0005120737	0.371	0.629

# Negatively associated
function_predictions %>%
  filter(mean <0) %>%
  arrange(-negative_support) %>%
  tt()

trait	mean	p1	p9	positive_support	negative_support
D08	-1.174084e-03	-0.0021635400	-0.0002216101	0.047	0.953
B03	-1.082132e-02	-0.0184920050	-0.0036627348	0.054	0.946
D06	-3.194052e-03	-0.0070018415	0.0001290343	0.116	0.884
B04	-8.373734e-03	-0.0174614803	0.0007363593	0.121	0.879
D07	-1.200153e-02	-0.0300385854	0.0042494388	0.185	0.815
B06	-6.669703e-03	-0.0166283189	0.0027105789	0.199	0.801
D05	-1.717352e-03	-0.0075185682	0.0038509242	0.216	0.784
S03	-9.563521e-03	-0.0333823692	0.0159823031	0.226	0.774
D03	-4.017320e-03	-0.0127303011	0.0040172389	0.236	0.764
B02	-3.462674e-03	-0.0124506768	0.0051573014	0.298	0.702
D09	-2.167099e-03	-0.0085278347	0.0046009115	0.299	0.701
S02	-4.696911e-03	-0.0151796566	0.0030908687	0.323	0.677
B07	-3.645464e-03	-0.0169431855	0.0096020470	0.361	0.639
D01	-3.640355e-04	-0.0053797539	0.0041408263	0.422	0.578
B10	-3.069883e-06	-0.0002900964	0.0002734412	0.473	0.527

positive <- function_predictions %>%
  filter(mean >0) %>%
  arrange(mean) %>%
  filter(positive_support>=0.9) %>%
  dplyr::select(-negative_support) %>%
  rename(support=positive_support)

negative <- function_predictions %>%
  filter(mean <0) %>%
  arrange(mean) %>%
  filter(negative_support>=0.9) %>%
  dplyr::select(-positive_support) %>%
  rename(support=negative_support)

bind_rows(positive,negative) %>%
  left_join(GIFT_db,by=join_by(trait==Code_function)) %>%
  mutate(trait=factor(trait,levels=c(rev(positive$trait),rev(negative$trait)))) %>%
  ggplot(aes(x=mean, y=fct_rev(trait), xmin=p1, xmax=p9, color=Function)) +
      geom_point() +
      geom_errorbar() +
      xlim(c(-0.02,0.02)) +
      geom_vline(xintercept=0) +
      scale_color_manual(values = c("#debc14","#440526","#dc7c17","#172742","#debc14","#440526","#dc7c17","#172742","#357379","#6c7e2c","#d8dc69","#774d35","#db717d")) +
      theme_minimal() +
      labs(x="Regression coefficient",y="Functional trait")